To address this resource limitation, we have previously proposed workload decomposition, which exploits the firing principle of LIF neurons, decomposing each neuron with many pre-synaptic connections into a sequence of homogeneous fanin-of-two (FIT) neural units [1]}. Figure REF  illustrates the spatial decomposition using a small example of a 3-input neuron shown in Figure REF (a). We consider the mapping of this neuron to 2x2 crossbars. Since each crossbar can accommodate a maximum of two pre-synaptic connections per neuron, the example 3-input neuron cannot be mapped to the crossbar directly. The most common solution is to eliminate a synaptic connection, which may lead to accuracy loss. Figure REF (b) illustrates the decomposition mechanism, where the 3-input neuron is implemented using two FIT neural units connected in sequence as shown in Figure REF (b). Each FIT unit is similar to a 2-input neuron and it exploits the leaky integrate behavior in hardware to maintain the functional equivalence between Figures REF (a) and REF (b). Finally, Figure REF (c) illustrates the mapping of the decomposed neuron utilizing two 2x2 crossbars. The functionality of the FIT neural units is implemented using the synaptic cells of the two crossbars.
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